Search Results for author:
Found 6 papers, 0 papers with code
LINDT: Tackling Negative Federated Learning with Local Adaptation
On occasion of NFL recovery, the framework makes adaptation to the federated model on each client's local data by learning a Layer-wise Intertwined Dual-model.
FRI-Miner: Fuzzy Rare Itemset Mining
In some cases, infrequent or rare itemsets and rare association rules also play an important role in real-life applications.
Databases
Federated Learning Attacks and Defenses: A Survey
For the purpose of advancing the research in this field, building a robust FL system, and realizing the wide application of FL, this paper sorts out the possible attacks and corresponding defenses of the current FL system systematically.
AI-Generated Content (AIGC): A Survey
To address the challenges of digital intelligence in the digital economy, artificial intelligence-generated content (AIGC) has emerged.
Data Scarcity in Recommendation Systems: A Survey
The prevalence of online content has led to the widespread adoption of recommendation systems (RSs), which serve diverse purposes such as news, advertisements, and e-commerce recommendations.
FL-GUARD: A Holistic Framework for Run-Time Detection and Recovery of Negative Federated Learning
Federated learning (FL) is a promising approach for learning a model from data distributed on massive clients without exposing data privacy.
